Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Instrument and Data Set
2.3. Theoretical Simulations
2.4. SAR Water Detection Method
3. Results
3.1. Theoretical Simulations
3.2. Land and Water Classifications
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Bianduo; Bianbaciren; Li, L.; Wang, W.; Zhaxiyangzong. The response of lake change to climate fluctuation in north Qinghai-Tibet Plateau in last 30 years. J. Geogr. Sci. 2009, 19, 131–142. [Google Scholar] [CrossRef]
- Ma, R.; Duan, H.; Hu, C.; Feng, X.; Li, A.; Ju, W.; Jiang, J.; Yang, G. A half-century of changes in China’s lakes: Global warming or human influence? Geophys. Res. Lett. 2010, 37. [Google Scholar] [CrossRef]
- Van Huissteden, J.; Berrittella, C.; Parmentier, F.J.W.; Mi, Y.; Maximov, T.C.; Dolman, A.J. Methane emissions from permafrost thaw lakes limited by lake drainage. Nat. Clim. Chang. 2011, 1, 119–123. [Google Scholar] [CrossRef]
- Zhang, G.; Xie, H.; Kang, S.; Yi, D.; Ackley, S.F. Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009). Remote Sens. Environ. 2011, 115, 1733–1742. [Google Scholar] [CrossRef]
- Yao, T.; Thompson, L.; Yang, W.; Yu, W.; Gao, Y.; Guo, X.; Yang, X.; Duan, K.; Zhao, H.; Xu, B.; et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Chang. 2012, 2, 663–667. [Google Scholar] [CrossRef]
- Du, J.; Kimball, J.S.; Jones, L.A.; Watts, J.D. Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS. Remote Sens. Environ. 2016, 184, 469–481. [Google Scholar] [CrossRef]
- Cheng, G.; Wu, T. Responses of permafrost to climate change and their environmental significance, Qinghai-Tibet Plateau. J. Geophys. Res. Earth Surf. 2007, 112, F02S03. [Google Scholar] [CrossRef]
- Yang, K.; Wu, H.; Qin, J.; Lin, C.; Tang, W.; Chen, Y. Recent climate changes over the Tibetan Plateau and their impacts on energy and water cycle: A review. Glob. Planet. Chang. 2014, 112, 79–91. [Google Scholar] [CrossRef]
- Liu, Q.; Du, J.; Shi, J.; Jiang, L. Analysis of spatial distribution and multi-year trend of the remotely sensed soil moisture on the Tibetan Plateau. Sci. China Earth Sci. 2013, 56, 2173–2185. [Google Scholar] [CrossRef]
- Jiang, Q.; Fang, H.; Zhang, J. Dynamic changes of lakes and the geo-mechanism in Tibet based on RS and GIS technology. In Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China; Tong, Q., Ed.; International Society for Optics and Photonics: Bellingham, WA, USA, 2008; Volume 7123, p. 71230S. [Google Scholar]
- Carroll, M.L.; Townshend, J.R.; DiMiceli, C.M.; Noojipady, P.; Sohlberg, R.A. A new global raster water mask at 250 m resolution. Int. J. Digit. Earth 2009, 2, 291–308. [Google Scholar] [CrossRef] [Green Version]
- Zhang, G.; Yao, T.; Xie, H.; Zhang, K.; Zhu, F. Lakes’ state and abundance across the Tibetan Plateau. Chin. Sci. Bull. 2014, 59, 3010–3021. [Google Scholar] [CrossRef]
- Wan, W.; Xiao, P.; Feng, X.; Li, H.; Ma, R.; Duan, H.; Zhao, L. Monitoring lake changes of Qinghai-Tibetan Plateau over the past 30 years using satellite remote sensing data. Chin. Sci. Bull. 2014, 59, 1021–1035. [Google Scholar] [CrossRef]
- Chen, J.; Chen, J.; Liao, A.; Cao, X.; Chen, L.; Chen, X.; He, C.; Han, G.; Peng, S.; Lu, M.; et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 2015, 103, 7–27. [Google Scholar] [CrossRef]
- Pekel, J.F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef] [PubMed]
- Ma, R.; Yang, G.; Duan, H.; Jiang, J.; Wang, S.; Feng, X.; Li, A.; Kong, F.; Xue, B.; Wu, J.; et al. China’s lakes at present: Number, area and spatial distribution. Sci. China Earth Sci. 2011, 54, 283–289. [Google Scholar] [CrossRef]
- Joshi, N.; Baumann, M.; Ehammer, A.; Fensholt, R.; Grogan, K.; Hostert, P.; Jepsen, M.R.; Kuemmerle, T.; Meyfroidt, P.; Mitchard, E.T.; et al. A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sens. 2016, 8, 70. [Google Scholar] [CrossRef]
- Prigent, C.; Papa, F.; Aires, F.; Rossow, W.B.; Matthews, E. Global inundation dynamics inferred from multiple satellite observations, 1993–2000. J. Geophys. Res. Atmos. 2007, 112, D12107. [Google Scholar] [CrossRef]
- Schroeder, R.; McDonald, K.C.; Chapman, B.D.; Jensen, K.; Podest, E.; Tessler, Z.D.; Bohn, T.J.; Zimmermann, R. Development and evaluation of a multi-year fractional surface water data set derived from active/passive microwave remote sensing data. Remote Sens. 2015, 7, 16688–16732. [Google Scholar] [CrossRef]
- Du, J.; Kimball, J.S.; Galantowicz, J.; Kim, S.B.; Chan, S.K.; Reichle, R.; Jones, L.A.; Watts, J.D. Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat. Remote Sens. Environ. 2018, 213, 1–17. [Google Scholar] [CrossRef] [PubMed]
- Solander, K.C.; Reager, J.T.; Famiglietti, J.S. How well will the Surface Water and Ocean Topography (SWOT) mission observe global reservoirs? Water Resour. Res. 2016, 52, 2123–2140. [Google Scholar] [CrossRef] [Green Version]
- Fu, L.L.; Alsdorf, D.; Rodriguez, E.; Morrow, R.; Mognard, N.; Lambin, J.; Vaze, P.; Lafon, T. The SWOT (Surface Water and Ocean Topography) Mission: Spaceborne radar interferometry for oceanographic and hydrological applications. In Proceedings of the OCEANOBS’09 Conference, Venice, Italy, 21–25 September 2009. [Google Scholar]
- Durand, M.; Fu, L.L.; Lettenmaier, D.P.; Alsdorf, D.E.; Rodriguez, E.; Esteban-Fernandez, D. The surface water and ocean topography mission: Observing terrestrial surface water and oceanic submesoscale eddies. Proc. IEEE 2010, 98, 766–779. [Google Scholar] [CrossRef]
- Fjortoft, R.; Gaudin, J.M.; Pourthie, N.; Lalaurie, J.C.; Mallet, A.; Nouvel, J.F.; Martinot-Lagarde, J.; Oriot, H.; Borderies, P.; Ruiz, C.; et al. KaRIn on SWOT: Characteristics of near-nadir Ka-band interferometric SAR imagery. IEEE Trans. Geosci. Remote Sens. 2014, 52, 2172–2185. [Google Scholar] [CrossRef]
- Gao, Q.; Li, Y.; Wan, Y.; Qin, X.; Jiangcun, W.; Liu, Y. Dynamics of alpine grassland NPP and its response to climate change in Northern Tibet. Clim. Chang. 2009, 97, 515. [Google Scholar] [CrossRef]
- Song, C.; Sheng, Y. Contrasting evolution patterns between glacier-fed and non-glacier-fed lakes in the Tanggula Mountains and climate cause analysis. Clim. Chang. 2016, 135, 493–507. [Google Scholar] [CrossRef]
- Mao, D.; Wang, Z.; Yang, H.; Li, H.; Thompson, J.R.; Li, L.; Song, K.; Chen, B.; Gao, H.; Wu, J. Impacts of climate change on Tibetan lakes: Patterns and processes. Remote Sens. 2018, 10, 358. [Google Scholar] [CrossRef]
- Zhu, Z.; Wang, S.; Woodcock, C.E. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens. Environ. 2015, 159, 269–277. [Google Scholar] [CrossRef]
- Qiu, S.; He, B.; Zhu, Z.; Liao, Z.; Quan, X. Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images. Remote Sens. Environ. 2017, 199, 107–119. [Google Scholar] [CrossRef]
- Wu, T.D.; Chen, K.S. A reappraisal of the validity of the IEM model for backscattering from rough surfaces. IEEE Trans. Geosci. Remote Sens. 2004, 42, 743–753. [Google Scholar]
- Fung, A.K.; Chen, K.S. An update on the IEM surface backscattering model. IEEE Geosci. Remote Sens. Lett. 2004, 1, 75–77. [Google Scholar] [CrossRef]
- Chen, K.S.; Wu, T.D.; Tsay, M.K.; Fung, A.K. Note on the multiple scattering in an IEM model. IEEE Trans. Geosci. Remote Sens. 2000, 38, 249–256. [Google Scholar] [CrossRef]
- Bindlish, R.; Barros, A.P. Multifrequency soil moisture inversion from SAR measurements with the use of IEM. Remote Sens. Environ. 2000, 71, 67–88. [Google Scholar] [CrossRef]
- Jiang, L.; Shi, J.; Tjuatja, S.; Dozier, J.; Chen, K.; Zhang, L. A parameterized multiple-scattering model for microwave emission from dry snow. Remote Sens. Environ. 2007, 111, 357–366. [Google Scholar] [CrossRef]
- Du, J.; Shi, J.; Sun, R. The development of HJ SAR soil moisture retrieval algorithm. Int. J. Remote Sens. 2010, 31, 3691–3705. [Google Scholar] [CrossRef]
- Baghdadi, N.; Gherboudj, I.; Zribi, M.; Sahebi, M.; King, C.; Bonn, F. Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements. Int. J. Remote Sens. 2004, 25, 3593–3623. [Google Scholar] [CrossRef]
- Dobson, M.C.; Ulaby, F.T.; Hallikainen, M.T.; El-Rayes, M.A. Microwave dielectric behavior of wet soil—Part II: Dielectric mixing models. IEEE Trans. Geosci. Remote Sens. 1985, 1, 35–46. [Google Scholar] [CrossRef]
- Kropáček, J.; Maussion, F.; Chen, F.; Hoerz, S.; Hochschild, V. Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data. Cryosphere 2013, 7, 287–301. [Google Scholar] [CrossRef] [Green Version]
- Stogryn, A. Equations for calculating the dielectric constant of saline water (correspondence). IEEE Trans. Microw. Theory Tech. 1971, 19, 733–736. [Google Scholar] [CrossRef]
- Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; CRC Press: Boca Raton, FL, USA, 2008. [Google Scholar]
Altitude | Frequency | Incidence Angles | Spatial Resolution |
400 km | 13.58 GHz | 2.5–7.5° | 40 m/200 m |
Swath Width | Look Direction | Baseline length | Cover area |
35 km | Right | 2.3 m | ±42 degrees of latitudes |
Frequency | Incidence Angles | RMS Height | Correlation Length | Soil Moisture | Correlation Function |
---|---|---|---|---|---|
13.58 GHz | 2, 5, and 8° | 0.125–3 cm interval 0.125 cm | 10 cm | 10–40% interval 15% (cm3/cm3) | Exponential function |
OLI Land | OLI Water | Total | |
---|---|---|---|
InIRA Land | 728,795 | 11,780 | 740,575 |
InIRA Water | 24,822 | 179,899 | 204,721 |
Total | 753,617 | 191,679 | 945,296 |
OLI Land | OLI Water | Total | |
---|---|---|---|
InIRA Land | 439,097 | 51,689 | 490,786 |
InIRA Water | 26,354 | 270,300 | 296,654 |
Total | 465,451 | 321,989 | 787,440 |
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Li, S.; Tan, H.; Liu, Z.; Zhou, Z.; Liu, Y.; Zhang, W.; Liu, K.; Qin, B. Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations. Remote Sens. 2018, 10, 1418. https://doi.org/10.3390/rs10091418
Li S, Tan H, Liu Z, Zhou Z, Liu Y, Zhang W, Liu K, Qin B. Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations. Remote Sensing. 2018; 10(9):1418. https://doi.org/10.3390/rs10091418
Chicago/Turabian StyleLi, Shengyang, Hong Tan, Zhiwen Liu, Zhuang Zhou, Yunfei Liu, Wanfeng Zhang, Kang Liu, and Bangyong Qin. 2018. "Mapping High Mountain Lakes Using Space-Borne Near-Nadir SAR Observations" Remote Sensing 10, no. 9: 1418. https://doi.org/10.3390/rs10091418